Optimization of the Thyristor Controlled Phase Shifting Transformer Using PSO Algorithm

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 8, No. 6, December 2018, pp. 5472~5483
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i6.pp5472-5483  5472
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Optimization of the Thyristor Controlled Phase Shifting
Transformer Using PSO Algorithm
Hadi Suyono1
, Rini Nur Hasanah2
, Paramita Dwi Putri Pranyata3
1,2
Department of Electrical Engineering, Faculty of Engineering, Brawijaya University, Indonesia
3
PT. Infineon Technologies, Indonesia
Article Info ABSTRACT
Article history:
Received Dec 29, 2017
Revised Jul 28, 2018
Accepted Aug 21, 2018
The increase of power system demand leads to the change in voltage profile,
reliability requirement and system robustness against disturbance. The
voltage profile can be improved by providing a source of reactive power
through the addition of new power plants, capacitor banks, or
implementation of Flexible AC Transmission System (FACTS) devices such
as Static VAR Compensator (SVC), Unified Power Flow Control (UPFC),
Thyristor Controlled Series Capacitor (TCSC), Thyristor Controlled Phase
Shifting Transformer (TCPST), and many others. Determination of optimal
location and sizing of device injection is paramount to produce the best
improvement of voltage profile and power losses reduction. In this paper,
optimization of the combined advantages of TCPST and TCSC has been
investigated using Particle Swarm Optimization (PSO) algorithm, being
applied to the 30-bus system IEEE standard. The effectiveness of the
placement and sizing of TCPST-TCSC combination has been compared to
the implementation of capacitor banks. The result showed that the
combination of TCPST-TCSC resulted in more effective improvement of
system power losses condition than the implementation of capacitor banks.
The power losses reduction of 46.47% and 42.03% have been obtained using
of TCPST-TCSC combination and capacitor banks respectively. The TCPST-
TCSC and Capacitor Bank implementations by using PSO algorithm have
also been compared with the implementation of Static VAR Compensator
(SVC) using Artificial Bee Colony (ABC) Algorithm. The implementation of
the TCSC-TCPST compensation with PSO algorithm have gave a better
result than using the capacitor bank with PSO algorithm and SVC with the
ABC algorithm.
Keyword:
Capacitor Bank
Losses reduction
PSO algorithm
TCPST
TCSC
Voltage profile improvement
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Hadi Suyono,
Department of Electrical Engineering, Faculty of Engineering,
Brawijaya University, Malang-Indonesia
Jl. MT. Haryono 167 Malang 65145 Indonesia.
Email: hadis@ub.ac.id
1. INTRODUCTION
The increase in electric power demand is in general proportional to the population growth of a
country. The load increase usually requires the addition of new power plants, both in terms of the number of
units and the generated power capacity. Consequently, further addition and expansion of transmission and
distribution systems infrastructure are required. The system becomes more complex and susceptible to
interference. Continuity of service must also satisfy technical and economical requirements. Load changes,
composition of generating units in operation as well as the changes in network configuration have a great
impact on the overall variation of voltage levels and power losses in the system. Voltage profile improvement
to fulfill the assigned operation standard of the entire system has some impacts to decrease power losses in

Int J Elec & Comp Eng ISSN: 2088-8708 
Optimization of the Thyristor Controlled Phase Shifting Transformer Using PSO Algorithm (Hadi Suyono)
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the system [1]-[2]. Voltage fluctuation can also be controlled through power system compensation. The
compensation both in transmission and distribution systems can be performed using either conventional
capacitor banks or Flexible Alternating Current Transmission Systems (FACTS) devices [3]-[6]. The FACTS
devices implementations include Static VAR Compensator (SVC) [7]-[9], Thyristor Controlled Series
Capacitor (TCSC) [10], Thyristor Controlled Phase Shifting Transformer (TCPST) [11], Unified Power Flow
Control (UPFC) [12], Dynamic Voltage Restorer (DVR) [13] and many others [14]. Some research results
also showed that the use of distributed generation could overcome the voltage profile problem and power
losses in distribution system, and also claimed to improve the distribution system performances in terms of
power quality [13], reliability [15-16], and stability [17]-[18].
This paper presents the investigation results on the use of combination of two FACTS devices,
which are a TCPST and a TCSC, to overcome the voltage profile problem. The TCSC is specifically used to
compensate the reactance of the transmission line and commonly used on long transmission line. The main
issues explored in the study were the optimal location and sizing for the placement of TCPST-TCSC
combination. Comparison to the use of conventional capacitor banks has been performed to justify. There
have been many methods proposed to solve the optimization problem, including the heuristic probabilistic
and artificial intelligent methods. Genetic Algorithm (AG) method was explored to determine the optimal
location of multi-type FACTS [19] and to control the voltage and reactive power [20]-[21]. There were many
other artificial intelligent methods such as Artificial Bee Colony algorithm [8], [22], Simulated Annealing
[23], Fuzzy EP algorithm [24], and others population algorithm [25], being studied. In this paper, Particle
Swarm Optimization (PSO) algorithm is used to solve the optimization problem. However, the PSO
algorithm has been used in many applications including the SVC location optimization [26]. PSO algorithm
is a method adopting the social behavior of birds when flying together in search of food. Performance of the
PSO algorithm to control the voltage profile and power losses have been tested on the 30-bus system IEEE
standard data.
2. POWER SYSTEM COMPENSATION
2.1. Thyristor Controlled Phase Shifting Transformer (TCPST)
TCPST is a device with characteristics similar to conventional Phase Angle Regulators (PAR),
being connected in series to network. The main difference lies in the fact that the tap changer is controlled
using thyristor to achieve faster operation [14]. The voltage on the primary side is injected thus the phase
shifted, and therefore the transmission angle can be controlled. The modeling of power injection at the ith
and
jth
buses is shown in Figure 1.
Psi + jQsi Psj + jQsj
Figure 1. Modeling of TCPST injection
The Psi, Qsi, Psj, and Qsj parameters can be determined as follows:
𝑃Si = 𝜏𝑏s𝑉i 𝑉j sin(𝜃ij + 𝛾) (1)
𝑄Si = 𝜏2
𝑏s𝑉i
2
− 𝜏𝑏s𝑉i 𝑉j cos(𝜃ij + 𝛾) (2)
𝑃Sj = −𝜏𝑏s𝑉i 𝑉j sin(𝜃ij + 𝛾) (3)
𝑄Sj = −𝜏𝑏s𝑉i 𝑉j cos(𝜃ij + 𝛾) (4)
where 𝜏 =
|𝑉S|
|𝑉i|
, 𝑏s =
1
𝑋s
, and γ is the angle to be controlled by TCPST. The voltage phase-angle
which can be controlled by TCPST is in the range between -5° and 5°. Figure 2 indicates the modeling of
TCPST in the transmission line [14].

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 8, No. 6, December 2018 : 5472 - 5483
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ji
ZLineŪTCPST
ŪTCPST = Vmin ∠±90° ~ Vmax ∠±90°
Figure 2. The TCSC model in transmission line
2.2. Thyristor Controlled Series Capacitor (TCSC)
TCSC is one type of FACTS devices which combines a Thyristor Controlled Reactor (TCR) with
capacitor [14]. The TCR consists of inductor being connected in series with thyristor. TCSC is capable to
adjust reactance of transmission line by controlling the thyristor firing-angle. Figure 3 represents a simple
modeling of series TCSC. To avoid over compensation, the injection of TCSC is set on 20% inductive
(0.2Xline) up to 70% capacitive (-0.7Xline) of the line reactance [28], such that:
rTCSCmin=-0.7 and rTCSCmax=0.2 (5)
i
Xc
Rij +jXij
TCSC
j
Figure 3. The modeling of TCSC
The TCSC modeling which enables the reactance control of the transmission line is shown in Figure
4 [28]. The relationship between the TCSC rating to the transmission line reactance is expressed as follows:
𝑋total = 𝑋line + 𝑋TCSC (6)
𝑋TCSC = 𝑟TCSC × 𝑋line (7)
where Xline is the line reactance and rTCSC is the compensation rating of TCSC.
i j
XTCSC ZLine
XTCSC = Xmin - Xmax
Figure 4. The TCSC modeling in the transmission system
3. PARTICLE SWARM OPTIMIZATION (PSO)
Particle Swarm Optimization (PSO) algorithm is an optimization technique within a problem space
which adopts the behavior of birds or fishes in finding food. In general, the PSO algorithm process to solve
an optimization problem can be represented using a flowchart shown in Figure 5. The steps to find the
optimum value using the PSO algorithm can be described as follows:
1. Initialize the particle positions randomly within a problem space.

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𝑃𝑎𝑟𝑡𝑖𝑐𝑙𝑒_𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 = 𝑀𝑖𝑛𝑖𝑚𝑢𝑚_𝐿𝑖𝑚𝑖𝑡 + (𝑀𝑎𝑥𝑖𝑚𝑢𝑚_𝐿𝑖𝑚𝑖𝑡 − 𝑀𝑖𝑛𝑖𝑚𝑢𝑚_𝐿𝑖𝑚𝑖𝑡) ×
𝑟𝑎𝑛𝑑 (1, 𝑃𝑎𝑟𝑡𝑖𝑐𝑙𝑒_𝑁𝑢𝑚𝑏𝑒𝑟) (8)
2. Initialize the velocity of each particle:
𝑉max =
(𝑀𝑎𝑥_𝑙𝑖𝑚𝑖𝑡 −𝑀𝑖𝑛𝑖𝑚𝑢𝑚_𝑙𝑖𝑚𝑖𝑡)
𝑁
(9)
𝑃𝑎𝑟𝑡𝑖𝑐𝑙𝑒_𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦 = (𝑉max − 𝑉min) × 𝑟𝑎𝑛𝑑(1, 𝑃𝑎𝑟𝑡𝑖𝑐𝑙𝑒_𝑁𝑢𝑚𝑏𝑒𝑟) + 𝑉min (10)
3. Evaluate the objective function of each particle.
4. Calculate the best position/location (Pbest) and the best global position (Gbest).
5. Update velocity and position of a particle, as shown in Equation (13) and (14). In this stage, the
acceleration coefficient c1 and c2 being used are generally within the values of 0 to 4. A weight function
(w), in the range of 0.4 to 0.9, is also used to control the exploration of global and local particles. The
improvement of the weight function can be done using Equation (11).
6.
𝑤(t) = (𝑤max − 𝑤min) × (
𝑖𝑡𝑒𝑟max−𝑖𝑡𝑒𝑟 (t)
𝑖𝑡𝑒𝑟max
) + 𝑤min (11)
𝑉id(t + 1) = 𝑤(t) × 𝑉id(t) + 𝑐1 × 𝑇1d(t) × (𝑃best id(t) − 𝑋id(t)) + 𝑐2 × 𝑇2d(t) × (𝐺bestd(t) −
𝑋id(t)) (12)
𝑋id(t + 1) = 𝑋id(t) + 𝑉id(t) (13)
where t is the iteration step, Vid (t) is the current velocity of the particle i in the dimension d at iteration
step t, Vid (t + 1) is the velocity of the particle i in the dimension d at iteration step t + 1, XID (t) is the
current position of particle i in the dimension d at the iteration step t, XID (t + 1) is the position of particle
i in dimension d at iteration t + 1, c1 is the acceleration constant 1 (cognitive constant), c2 is the
acceleration constant 2 (social constant) , T1D (t) and T2D (t) are random numbers uniformly distributed
between 0 and 1, Pbestid (t) is the local best position of particle i in dimension d at iteration t, and Gbestid(t)
is the local best position of the global at iteration t.
7. Evaluate the objective function value on the next iteration.
8. Determine the final of Pbest and Gbest
9. Evaluate whether the solution is optimal, if a convergence has been achieved then it comes to end,
otherwise going back to step 3.
Figure 5. Steps to follow using the PSO algorithm

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The flowchart of the PSO algorithm implementation is shown in Figure 6, whereas the control
parameters used is given Table 1.
Figure 6. PSO solution
Table 1. The PSO control parameters
Parameter Value
Number of Particle 50
Maximum Iteration 20
Number of Variable 3
c1 and c2 4
weight (w) 0,4
rTCSC maximum 0,2 Xline
rTCSC minimum - 0,7 Xline
δTCPST maximum 5°
δTCPST minimum - 5°
Objective Function Min F=min Ploss
4. RESULTS AND DISCUSSION
In this study, the proposed solution has been simulated and tested using 30-bus system IEEE
standard data. The results of the power flow analysis have been obtained from some scenarios including the
conditions without any injection of compensation, the condition with injection of optimum capacitor bank
compensation, and the condition with injection of optimum TCPST-TCSC compensation. The PSO algorithm
implementation to determine the optimum location and sizing of the compensation using the TCPST-TCSC
combination has been compared to that using capacitor bank. In addition, the PSO algorithm performance has
also been compared with the implementation of Artificial Bee Colony (ABC) Algorithm [8].
4.1. IEEE 30 Bus System Data
The performance of the PSO algorithm has been tested using the 30-bus system IEEE standard data
as follows: Bus Number: 30, Slack bus: Bus no#1, Generator: Bus no# 2, 5, 8, 11, and 13, Total demand:
201.43 MW and 137.80 MVAR. The PSO algorithm will determine the best location and sizing of the
reactive power required in the system by performing the load flow analysis for each iteration. There are four

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(4) cases of the system that will be performed to show the performance of the PSO algorithm and the
implemented of the compensator devices i.e.:
- Case#1: without any injection of compensation,
- Case#2: with injection of capacitor bank compensation
- Case#3: with injection TCPST-TCSC compensation
- Case#4: with injection SVC compensation by using another optimization method i.e. Artificial Bee
Colony (ABC) Algorithm [8].
For Case#2 and #3, the load flow analyses have been performed after the determination of optimum
location and capacity of reactive power compensation required in the system using the PSO algorithm. In
addition, the PSO Algorithm is also compared with ABC algorithm as given in Case#4.
4.2. Results of Case#1: without any injection of compensation
In this analysis, the 30-bus IEEE system has been tested under the condition without any
compensating device. Based on the load flow analysis, the power flow as well as power losses on each
branch, and the voltage on each system bus could be determined. Newton Raphson algorithm has been
adopted to solve the load flow problem based on the following parameters: base power=100 MVA,
accuracy=0.00001, and maximum iteration number=20. The results of load flow analysis for this case is
shown in Table 2.
Based on Table 2, total active and reactive power generated were around 205.489 MW and 118.401
MVAR respectively. In addition, the total active and reactive power losses in the system were approximately
4.061 MW and -19.399 MVAR. There were three buses experiencing under voltage problem, being lower
than the allowable minimum voltage, i.e. 0.95 p.u. The buses with voltage level beyond the limits occurred
on bus# 18, bus#19, and bus#20. The percentage of the active power losses is about 2.02% with respect to the
active power load.
Table 2. Load flow results Case#1
Bus#
Voltage Load Generation
|V| (pu) P (MW)
Q
(MVAR)
P (MW)
Q
(MVAR)
1 1 0 0 0 24.819 -1.742
2 1 -0.334 21.7 12.7 60.97 30.371
3 0.983 -1.353 2.4 1.2 0 0
4 0.979 -1.597 7.6 1.6 0 0
5 0.983 -1.767 0 0 0 0
6 0.971 -2.05 0 0 0 0
7 0.967 -2.494 22.8 10.9 0 0
8 0.958 -2.531 30 30 0 0
9 0.963 -2.531 0 0 0 0
10 0.96 -2.789 5.9 2 0 0
11 0.963 -2.531 0 0 0 0
12 0.975 -1.346 11.2 7.5 0 0
13 1 1.7 0 0 37 18.855
14 0.962 -2.055 6.2 1.6 0 0
15 0.962 -1.898 8.2 2.5 0 0
16 0.96 -2.263 3.5 1.8 0 0
17 0.954 -2.882 9 5.8 0 0
18 0.914 -2.133 3.2 0.9 0 0
19 0.889 -2.027 9.5 34 0 0
20 0.905 -2.33 2.2 0.7 0 0
21 0.971 -2.877 19.669 11.2 0 0
22 0.98 -2.708 0 0 31.59 36.954
23 1 -1.603 3.2 1.6 22.2 20.463
24 0.975 -2.752 15 6.7 0 0
25 0.984 -2.015 1 0 0 0
26 0.966 -2.464 3.5 2.3 0 0
27 1 -1.154 0 0 28.91 13.5
28 0.973 -2.122 0 0 0 0
29 0.976 -2.808 3.659 0.9 0 0
30 0.964 -3.803 12 1.9 0 0
Total 201.428 137.8 205.489 118.401
Total Losses 4.061 MW -19.399 MVAR
% of Losses 2.02% MW -16.4% MVAR

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4.3. Results of Case#2: with injection of capacitor bank compensation
IEEE standard system has been tested for the condition with the placement of capacitor bank. The
number of capacitor banks required in the system was 3 locations with capacity values between 0 up to 50
MVAR. The optimum location and capacity of capacitor bank used are shown in Table 3. From the table it
can be seen that the resulted location and capacity of capacitor bank after optimization using the PSO
algorithm were 12.5433 MVAR (0.2502 p.u.) located at bus#17, 25.5432 MVAR (0.5105 p.u.) located at
bus#7, and 28.2801 MVAR (0.5664 p.u.) located at bus#31.
Table 3. Capacitor bank optimization results for Case#2 using the PSO algorithm
Compensation Bus No# Location Rating (p.u.) Rating (MVAR)
Capacitor Bank 17 0.2502 12.5433
Table 4 shows the simulation result of the system after the optimum placement of the capacitor
banks with the minimum power losses. It can be seen that the total power generated were around 213.83MW
for active power and 149.91 MVAR for reactive power. The total active and reactive power losses for Case#2
were 9.901 MW and 16.009 MVAR respectively. There is no voltage violation for the Case#2 since all of bus
voltage values were in the range of 0.95 p.u. to 1.05 p.u. The voltage profile for each bus is depicted in
Figure 8. The voltage level of buses which experienced under voltage condition in Case # 1 has been rising to
meet the allowable voltage level.
Table 4. Load flow results for Case#2
Bus#
|V| (pu)  P (MW) Q (MVAR) P (MW) Q (MVAR)
1 1 0 0 0 23.112 -10.156
2 1 -0.288 21.7 12.7 60.97 6.641
3 0.998 -1.52 2.4 1.2 0 0
4 0.997 -1.813 7.6 1.6 0 0
5 0.991 -1.796 0 0 0 0
6 0.987 -2.224 0 0 0 0
7 0.98 -2.602 22.8 10.9 0 0
8 0.974 -2.68 30 30 0 0
9 0.997 -2.743 0 0 0 0
10 0.989 -3.018 5.9 2 0 0
11 0.997 -2.743 0 0 0 0
12 0.989 -1.373 11.2 7.5 0 0
13 1 1.628 0 0 37 8.645
14 0.979 -2.077 6.2 1.6 0 0
15 0.982 -2.015 8.2 2.5 0 0
16 0.981 -2.401 3.5 1.8 0 0
17 0.981 -3.059 9 5.8 0 0
18 0.969 -3.08 3.2 0.9 0 0
19 0.965 -3.503 9.5 34 0 0
20 0.97 -3.45 2.2 0.7 0 0
21 0.994 -2.944 19.669 11.2 0 0
22 1 -2.73 0 0 31.59 26.998
23 1 -1.134 3.2 1.6 22.2 6.703
24 0.985 -2.548 15 6.7 0 0
25 0.988 -1.87 1 0 0 0
26 0.97 -2.315 3.5 2.3 0 0
27 1 -1.051 0 0 28.91 8.488
28 0.988 -2.236 0 0 0 0
29 0.976 -2.705 3.659 0.9 0 0
30 0.964 -3.7 12 1.9 0 0
Total 201.428 137.8 203.782 47.319
% of Losses 1.17% -17.5%
The process in achieving the minimum power losses using the PSO algorithm is shown in Figure 7.
The minimum power loss on each iteration as the objective function has been recorded. It is only after
reaching the convergence criteria that the PSO algorithm steps could be ended. The maximum iterations

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performed for the test were 20 iterations. Based on the figure, it also showed that the minimum active power
losses can be reached on the 5th
iteration with the value of 2.354 MW.
Figure 7. The convergence criteria for Case#2
4.4. Result of Case#3: with injection TCSC-TCPST compensation
In this part, the standard IEEE 30-bus system has been tested in a condition with TCSC-TCPST
being implemented in the system. Based on the results, the optimum location and size of the TCSC-TCPST is
are shown in Table 5. The optimum place of TCSC was on the line#15 which was connected to bus#27 and
bus#29 with a rating of -0,5535Xline and line#31 which was connected to bus#27 and bus#30 with the rating
of -0,5343Xline. In addition, the TCPST has also been implemented in the line#26 which was connected to
bus#10 and bus#20 with the injected phase-angle of -2.6839°.
The simulation results of the system after the placement of TCSC and TCPST for the most
minimum power loss is shown in Table 6. It can be seen that the total power generated by the generator was
equal to 203.602 MW and 40.891 MVAR for active power and reactive power respectively. The total active
and reactive power losses for Case#3 were 2.174 MW and -25.041 MVAR respectively. There was no
voltage violation for the Case#3 since the voltage of all buses were in the range of 0.95 p.u. – 1.05 p.u. The
percentage of the power losses is about 1.07% with respect to the active power load.
The process in achieving the minimum power losses for Case#3 with TCSC-TCPST compensation
using the PSO algorithm is given in Figure 8. The minimum active power losses could be reached on the 14th
iteration with the value of 2.174 MW.
Figure 8. The convergence criteria for Case#3

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Table 5. TCSC & TCPST optimization results using the PSO algorithm for Case#3
Compensation Location (Line) From Bus To Bus Rating
TCSC 15 27 29 -0,5535Xline
TCSC 31 27 30 -0,5343Xline
TCPST 26 10 20 -2,6839°
Table 6. Load flow results for Case#3
Bus#
1 1 0 0 0 22.932 -8.16
2 1 -0.284 21.7 12.7 60.97 5.876
3 0.995 -1.467 2.4 1.2 0 0
4 0.993 -1.746 7.6 1.6 0 0
5 0.993 -1.81 0 0 0 0
6 0.991 -2.269 0 0 0 0
7 0.983 -2.633 22.8 10.9 0 0
8 0.988 -2.887 30 30 0 0
9 0.99 -2.753 0 0 0 0
10 0.989 -3.009 5.9 2 0 0
11 0.99 -2.753 0 0 0 0
12 0.993 -1.393 11.2 7.5 0 0
13 1 1.597 0 0 37 5.978
14 0.993 -2.445 6.2 1.6 0 0
15 0.989 -2.109 8.2 2.5 0 0
16 0.983 -2.398 3.5 1.8 0 0
17 0.982 -3.054 9 5.8 0 0
18 0.983 -3.366 3.2 0.9 0 0
19 0.983 -3.904 9.5 34 0 0
20 0.983 -3.739 2.2 0.7 0 0
21 0.994 -2.93 19.669 11.2 0 0
22 1 -2.714 0 0 31.59 26.712
23 1 -1.068 3.2 1.6 22.2 3.302
24 0.985 -2.524 15 6.7 0 0
25 0.988 -1.884 1 0 0 0
26 0.97 -2.329 3.5 2.3 0 0
27 1 -1.088 0 0 28.91 7.183
28 0.993 -2.303 0 0 0 0
29 0.976 -2.743 3.659 0.9 0 0
30 0.964 -3.738 12 1.9 0 0
Total 201.428 137.8 203.602 40.891
% of Losses 1.07% -18.2%
4.5. Result of Case#4: with injection SVC using Artificial Bee Colony (ABC) Algorithm [8]
To show the performance of the PSO algorithm, the comparison with Artificial Bee Colony (ABC)
Algorithm [8] result has been made. The same simulation using IEEE 30-bus system as a base-case data have
been made and compared in terms of voltage profile and the best active power loss reached. Based on the
ABC algorithm result shows that the number of SVCs required in the system was two (2) locations with
capacity values 36.996 MVAR at bus#5 and 36.971 MVAR at bus#19 respectively.
The simulation results of the system after the placement of SVC for the most minimum power loss
is shown in Table 7. It can be seen that the total power generated by the generator was equal to 204.21 MW
and 48.40 MVAR for active power and reactive power. The total active and reactive power losses obtained
for this case were 2.7927 MW and -15.2076 MVAR respectively. There was no voltage violation in this
case since the voltage of all buses were in the acceptable range. The percentage of the power losses is about
1.37% with respect to the active power load.
Table 7. Load flow results for Case#4: with injection SVC using ABC Algorithm [8]
Bus#
1 1 0 0 0.00 23.54 -5.64
2 1 -0.369 21.7 12.70 60.97 1.60
3 0.9883 -1.535 2.4 1.20 0.00 0.00
4 0.986 -1.786 7.6 1.60 0.00 0.00

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Table 7. Load flow results for Case#4: with injection SVC using ABC Algorithm [8]
Bus#
5 1.0223 -2.472 0 0.00 0.00 0.00
6 0.9815 -2.246 0 0.00 0.00 0.00
7 0.9885 -2.846 22.8 10.90 0.00 0.00
8 0.9694 -2.709 30 30.00 0.00 0.00
9 0.979 -2.825 0 0.00 0.00 0.00
10 0.9776 -3.129 5.9 2.00 0.00 0.00
11 0.979 -2.825 0 0.00 0.00 0.00
12 0.986 -1.415 11.2 7.50 0.00 0.00
13 1 1.5966 0 0.00 37.00 10.95
14 0.9775 -2.14 6.2 1.60 0.00 0.00
15 0.9815 -2.106 8.2 2.50 0.00 0.00
16 0.9745 -2.472 3.5 1.80 0.00 0.00
17 0.9708 -3.181 9 5.80 0.00 0.00
18 0.9733 -3.426 3.2 0.90 0.00 0.00
19 0.9727 -3.99 9.5 34.00 0.00 0.00
20 0.9727 -3.834 2.2 0.70 0.00 0.00
21 0.9746 -3.507 19.669 11.20 0.00 0.00
22 1 -2.072 0 0.00 31.59 24.64
23 1 -0.962 3.2 1.60 22.20 6.94
24 0.9848 -2.159 15 6.70 0.00 0.00
25 0.988 -1.648 1 0.00 0.00 0.00
26 0.97 -2.1 3.5 2.30 0.00 0.00
27 1 -0.928 0 0.00 28.91 9.92
28 0.9831 -2.291 0 0.00 0.00 0.00
29 0.9762 -2.531 3.659 0.90 0.00 0.00
30 0.9637 -3.538 12 1.90 0.00 0.00
Total 201.428 137.80 204.21 48.40
% of Losses 1.37% -31.42%
4.6. Comparison results between cases
The results of system simulation on IEEE 30-bus system with four cases have been compared in
terms of voltage profile and the best active power loss reached for each case by using the PSO algorithm and
ABC algorithm [8]. Figure 8 shows the voltage profiles comparison. The worst voltage profile has been
experienced in Case#1 where the availability of the reactive power was very limited depending on the
generating unit. There are three buses were below the allowable minimum voltage of 0.95 p.u.
However, the supply of the reactive power depended on the output of the active power loading for
each unit. On the other hand, the voltage profiles have been improved in Case#2 and Case#3 by using PSO
algorithm and Case#4 by using ABC algorithm, since the reactive power sources have been implemented to
support the deficiency of the reactive power. All of the buses voltage was above 0.95 p.u., since the reactive
power compensations required in the system were satisfied using capacitor bank for Case#2, TCSC-TCPST
for Case#3, and SVC for Case#4. The Case#3 showed a better voltage profile compared to the other cases.
Figure 8. Voltage profile comparison for each case

 ISSN: 2088-8708
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Table 7. Comparison of active power losses between cases
Simulation
Case#1 (No-
Comp)
Case#2 (Cap.
Bank - PSO)
Case#3 (TCSC +
TCPST – PSO)
Case#4 (SVC -
ABC) [8]
Active Power Losses (MW) (a) 4.061 2.354 2.174 2.793
% of (a) respect to active power load 2.02% 1.17% 1.07% 1.37%
Active power losses improvement respect to
Case#1 (MW)
0.000 1.707 1.887 1.268
Percentage of active power losses improvement
respect to Case#1 (%)
0.00% 42.03% 46.47% 31.23%
Table 7 shows the comparison of active power losses for the four cases. The percentage of active
power losses referred to the total active power load is given in the table. The implementation of the capacitor
banks and the TCSC-TCPST combination which had been optimized using the PSO algorithm could reduce
the active power losses as much as 1.707 MW (42.03%) and 1.887 MW (46.47%) respectively, being
compared to the condition without any injection of the compensation (Case#1). In addition, the
implementation of the SVC compensation by using the ABC algorithm could also reduce the active power
losses as much as 1.887 MW (31.23%) with respect to the Case#1. However, the implementation of the
TCSC-TCPST compensation with PSO algorithm gave a slightly better result than using the capacitor bank
with PSO algorithm and SVC with ABC algorithm [8].
5. CONCLUSION
The Particle Swarm Optimization (PSO) algorithm has been explored to determine the optimum
location and sizing of capacitor banks and TCPST-TCSC combination with the aim to improve the voltage
profile and to reduce the power losses of the system. It has been proven that the algorithm performed well
and required small iterations number, i.e. 5-14 iterations, in determining the optimum values. The 30-bus
system IEEE standard was tested to show the performance of the PSO algorithm. The optimum placement
and sizing of capacitor bank and TCPST-TCSC combination on the transmission lines using the PSO
algorithm have been proven to be able to improve the bus voltage bus values within the allowable voltage
limits. The result also showed that the implementation of optimum TCPST-TCSC placement was more
effective than the optimum placement of capacitor banks in reducing power losses of the system, giving a
reduction of 46.47% and 42.03% using TCPST-TCSC combination and capacitor bank respectively. The
performance of PSO algorithm also have been compared with the ABC algorithm. The implementation of the
TCSC-TCPST compensation with PSO algorithm gave a slightly better result than using the capacitor bank
with PSO algorithm and SVC with the ABC algorithm.
ACKNOWLEDGEMENTS
We are grateful to the Institute of Research and Community Service of Brawijaya University for the
funding of the research the results of which are presented in this publication and for the Power System
Engineering and Energy Management Research Group (PseeMRG), Faculty of Engineering, Brawijaya
University, for the funding of this publication.
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